| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1083 | | totalAiIsmAdverbs | 4 | | found | | 0 | | adverb | "reluctantly" | | count | 1 |
| | 1 | | | 2 | | | 3 | |
| | highlights | | 0 | "reluctantly" | | 1 | "suddenly" | | 2 | "precisely" | | 3 | "carefully" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 26.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1083 | | totalAiIsms | 16 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | word | "sent shivers down" | | count | 1 |
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| | highlights | | 0 | "gleaming" | | 1 | "scanned" | | 2 | "chaotic" | | 3 | "charm" | | 4 | "comfortable" | | 5 | "flicker" | | 6 | "eyebrow" | | 7 | "warmth" | | 8 | "velvet" | | 9 | "silk" | | 10 | "absolutely" | | 11 | "whisper" | | 12 | "stomach" | | 13 | "unreadable" | | 14 | "sent shivers down" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 81 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "managed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 1083 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 635 | | uniqueNames | 8 | | maxNameDensity | 0.94 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Well | 1 | | Golden | 1 | | Empress | 1 | | Moreau | 1 | | Eva | 3 | | Aurora | 5 | | London | 1 | | Lucien | 6 |
| | persons | | 0 | "Moreau" | | 1 | "Eva" | | 2 | "Aurora" | | 3 | "Lucien" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 51.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 2 | | matches | | 0 | "as if trying to determine if this expensive intrusion was friend or foe" | | 1 | "move that seemed to subtly claim territory" |
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| 15.33% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.847 | | wordCount | 1083 | | matches | | 0 | "Not with size, though he was tall enough, but with an aura of expensive tailoring" | | 1 | "Not cursed, precisely, but…attuned to certain energies" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 23.54 | | std | 14.5 | | cv | 0.616 | | sampleLengths | | 0 | 39 | | 1 | 68 | | 2 | 22 | | 3 | 33 | | 4 | 23 | | 5 | 46 | | 6 | 15 | | 7 | 21 | | 8 | 39 | | 9 | 20 | | 10 | 42 | | 11 | 8 | | 12 | 17 | | 13 | 33 | | 14 | 13 | | 15 | 32 | | 16 | 38 | | 17 | 24 | | 18 | 17 | | 19 | 53 | | 20 | 31 | | 21 | 13 | | 22 | 14 | | 23 | 5 | | 24 | 36 | | 25 | 4 | | 26 | 51 | | 27 | 26 | | 28 | 32 | | 29 | 7 | | 30 | 23 | | 31 | 19 | | 32 | 10 | | 33 | 15 | | 34 | 3 | | 35 | 29 | | 36 | 11 | | 37 | 17 | | 38 | 11 | | 39 | 20 | | 40 | 26 | | 41 | 40 | | 42 | 9 | | 43 | 16 | | 44 | 7 | | 45 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 109 | | matches | | |
| 92.29% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 113 | | ratio | 0.018 | | matches | | 0 | "A flicker of something – amusement, maybe?" | | 1 | "– crossed his face." |
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| 88.15% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 635 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.05354330708661417 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.01889763779527559 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 9.58 | | std | 6.69 | | cv | 0.698 | | sampleLengths | | 0 | 8 | | 1 | 8 | | 2 | 4 | | 3 | 7 | | 4 | 7 | | 5 | 5 | | 6 | 7 | | 7 | 18 | | 8 | 4 | | 9 | 8 | | 10 | 3 | | 11 | 2 | | 12 | 11 | | 13 | 15 | | 14 | 20 | | 15 | 2 | | 16 | 1 | | 17 | 18 | | 18 | 10 | | 19 | 4 | | 20 | 6 | | 21 | 5 | | 22 | 8 | | 23 | 4 | | 24 | 5 | | 25 | 20 | | 26 | 7 | | 27 | 14 | | 28 | 5 | | 29 | 10 | | 30 | 16 | | 31 | 5 | | 32 | 8 | | 33 | 12 | | 34 | 15 | | 35 | 3 | | 36 | 1 | | 37 | 15 | | 38 | 5 | | 39 | 10 | | 40 | 28 | | 41 | 2 | | 42 | 2 | | 43 | 3 | | 44 | 5 | | 45 | 8 | | 46 | 9 | | 47 | 7 | | 48 | 4 | | 49 | 22 |
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| 72.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.46017699115044247 | | totalSentences | 113 | | uniqueOpeners | 52 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | | ratio | 0.014 | |
| 41.62% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 74 | | matches | | 0 | "She pulled the door inward," | | 1 | "she didn’t even know what." | | 2 | "He held his cane, the" | | 3 | "he said, the single syllable" | | 4 | "She lifted her chin, forcing" | | 5 | "He didn’t offer a reciprocal" | | 6 | "She stepped back, reluctantly granting" | | 7 | "she said, unnecessarily" | | 8 | "He scanned the flat, his" | | 9 | "It held the scent of" | | 10 | "He leaned his cane against" | | 11 | "She crossed her arms, tucking" | | 12 | "He didn’t flinch." | | 13 | "She raised an eyebrow" | | 14 | "Her life was a magnet" | | 15 | "He actually smiled, a brief," | | 16 | "It was the first genuine" | | 17 | "She didn’t want to believe" | | 18 | "she asked, her voice softer" | | 19 | "He paused, his gaze locking" |
| | ratio | 0.446 | |
| 47.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 74 | | matches | | 0 | "The third deadbolt clicked, then" | | 1 | "Aurora hadn’t expected him to" | | 2 | "She pulled the door inward," | | 3 | "she didn’t even know what." | | 4 | "Lucien stood in the hallway," | | 5 | "Charcoal suit, of course." | | 6 | "Platinum hair slicked back, revealing" | | 7 | "Amber and black, assessing her" | | 8 | "He held his cane, the" | | 9 | "he said, the single syllable" | | 10 | "She lifted her chin, forcing" | | 11 | "He didn’t offer a reciprocal" | | 12 | "Lucien Moreau never did things" | | 13 | "The hallway wasn’t exactly spacious." | | 14 | "Ptolemy, Eva’s tabby, wound around" | | 15 | "She stepped back, reluctantly granting" | | 16 | "The scent of his cologne," | | 17 | "she said, unnecessarily" | | 18 | "He scanned the flat, his" | | 19 | "Aurora countered, a little defensively" |
| | ratio | 0.824 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 74 | | matches | | 0 | "As if six months of" | | 1 | "Even now, months later, the" |
| | ratio | 0.027 | |
| 6.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 4 | | matches | | 0 | "Amber and black, assessing her with a coolness that felt practiced." | | 1 | "As if six months of ramen dinners and ten-hour shifts delivering Golden Empress menus hadn’t left their mark." | | 2 | "Ptolemy, Eva’s tabby, wound around his ankles as if trying to determine if this expensive intrusion was friend or foe." | | 3 | "Black silk, intricately beaded, with a neckline that plunged dangerously low." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 6 | | matches | | 0 | "she said, unnecessarily" | | 1 | "she asked, her voice softer now" | | 2 | "He paused, his gaze locking with hers" | | 3 | "she said, her voice sharp" | | 4 | "she managed, her voice barely a whisper" | | 5 | "she said, her voice raspy" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 48 | | tagDensity | 0.146 | | leniency | 0.292 | | rawRatio | 0.143 | | effectiveRatio | 0.042 | |